Summary of "Intermediate Python for Finance" from Datacamp.Org (https://gist.github.com/misho-kr/873ddcc2fc89f1c96414de9e0a58e0fe)

Intermediate Python for Finance.md

Intermediate Python for Finance

Learn how to use Python data structures, execution control statements, and DataFrames to manipulate financial data. Work with pandas, using data from the Federal Reserve Bank, to explore national economic trends—an essential part of understanding investment strategies. Calculate risk based on stock price data, and display this data in easy to read plots.

By Kennedy Behrman, Data Engineer, Author, Founder

Datetimes and Dictionaries

Create and manipulate Python datetime objects to help you identify key financial events, such as Black Friday. Store and efficiently look up items using Python dictionaries.

• Datetimes
  • Datetime from string
  • String from datetime
  • Datetime attributes
  • Comparing datetimes
    • Subtraction returns a timedelta object
    • Creating relative datetimes
• Dictionaries
  • Creating, adding to and deleting from

from datetime import datetime

black_monday = datetime(1987, 10, 19)
datetime.now()

black_monday_str = "Monday, October 19, 1987. 9:30 am"
format_str = "%A, %B %d, %Y. %I:%M %p"

datetime.datetime.strptime(black_monday_str, format_str)
dt.strftime(format_string)

from datetime import timedelta

offset = timedelta(weeks = 1)
cur_week = last_week + timedelta(weeks=1)

Control Flow and Logic

Use boolean logic to determine truth and use comparison and equality operators to control execution in if-statements and loops.

• Comparison operators
  • Comparing datetimes
  • Comparing dictionaries
  • Comparing different types
• Boolean operators
  • Object evaluation
  • AND and OR operators
  • Short circuit
  • NOT operator
  • Returning objects
• IF statements
• for and while loops
  • Skipping with continue
  • Stopping with break

Pandas Dataframe

Create and access DataFrames with pandas using financial data from other data structures, including Dicts, lists, and CSV files. Aggregate data across rows or columns, calculate averages, and extend your data using functions.

• Creating Pandas DataFrame
  • From dictionary, list of dictionaries, or list of lists
• Reading data
• Accessing Data
  • Access column using brackets
  • Access column using dot-syntax
  • Access multiple columns
  • Access rows using brackets
  • loc() and iloc()
  • Columns with loc()
• Setting a single value, multiple values or multiple columns
• Aggregating and summarizing
  • DataFrame methods
    • .count(), min(), max(), first(), last(), sum(), prid(), max()
    • .mean(), media(), std(), var()
  • Axis: default axis=0, axis=rows, or axis=1, axis=columns
• Extending and manipulating data
  • Adding and removing columns
  • Operations on DataFrames
  • apply()

import pandas as pd

pd.DataFrame()
data = {'Bank Code': ['BA', 'AAD', 'BA'],
        'Account#': ['ajfdk2', '1234nmk', 'mm3d90'],
        'Balance':[1222.00, 390789.11, 13.02]}
df = pd.DataFrame(data=data)

data = [['BA', 'ajfdk2', 1222.00],
        ['AAD', '1234nmk', 390789.11],
        ['BA', 'mm3d90', 13.02]]
columns = ['Bank Code', 'Account#', 'Balance']
df = pd.DataFrame(data=data, columns=columns)

df = pd.read_csv('/data/daily/transactions.csv', sep='|')

accounts['Balance']
accounts.Balance
accounts[['Bank Code', 'Account#']]
accounts[0:2]
accounts[[True, False, True]]

accounts.loc['b']
accounts.loc[['a','c']]
df.loc[[True, False, True]]

accounts.loc['a':'c', ['Balance','Account#']]
accounts.loc['a':'c',[True,False,True]]
accounts.loc['a':'c','Bank Code':'Balance']

accounts.iloc[0:2, [0,2]]

accounts.loc['a', 'Balance'] = 0
accounts.iloc[:2, 1:] = 'NA'

df.count()
df.sum(axis=1)

df.loc[:,'AAD'].max()
df.iloc[0].min()

pce['PCE'] = pce['PCDG'] + pce['PCND'] + pce['PCESV']
pce.drop(columns=['PCDG', 'PCND', 'PCESV'], axis=1, inplace=True)

pce.append(new_row)

all_rows = [row1, row2, row3, pce]
pd.concat(all_rows)

gdp['GDP'] = gdp.apply(np.sum, axis=1)

Working with NASDAQ Stock Data

Working with real-world NASDAQ stock data to interpret new data, create masks to filter data, and visualize your findings with plots.

• Peeking at data with head(), tail(), and describe()
• Filtering data
  • Column comparison
  • Masking by symbol
  • Pandas boolean operators
  • Combining conditions
• Plotting data
  • Plot types: bar, barh, line, scatter, box, histogram, kde

import pandas as pd

aapl.head(3)

aapl.describe()
aapl.describe(include='object')
aapl.describe(include=['float', 'object'])
aapl.describe(include='all')

aapl.describe(percentiles=[.1, .5, .9])
aapl.describe(exclude='float')

prices.High > 2160

mask_symbol = prices.Symbol == 'AAPL'
aapl = prices.loc[mask_symbol]

mask_prices = prices['Symbol'] != 'AMZN'
mask_date = historical_highs['Date'] > datetime(2020, 4, 1)
mask_amzn = mask_prices & mask_date
prices.loc[mask_amzn]

my_dataframe.plot()
exxon.set_index('Date', inplace=True)
exxon.plot(x='Date', y='High', rot=90, title='Exxon Stock Price')

exxon.plot(y='High',kind='hist')

Select the Balance for October 2nd

print(ledger.loc['',''])

Select the Balance for October 3rd

print(ledger.['','____'])